Philippine vehicle plate localization using image thresholding and genetic algorithm
This paper proposes a vehicle plate localization method using genetic algorithm integrated with image thresholding. Image thresholding outputs a value which varies on the time the image is captured. Genetic algorithm on the other hand, executed the license plate region detection of the digital image...
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oai:animorepository.dlsu.edu.ph:faculty_research-43582023-01-09T09:26:31Z Philippine vehicle plate localization using image thresholding and genetic algorithm Bedruz, Rhen Anjerome Sybingco, Edwin Bandala, Argel A. Quiros, Ana Riza Uy, Aaron Christian P. Dadios, Elmer P. This paper proposes a vehicle plate localization method using genetic algorithm integrated with image thresholding. Image thresholding outputs a value which varies on the time the image is captured. Genetic algorithm on the other hand, executed the license plate region detection of the digital image which depends on the set-level of the image threshold values obtained. Using the proposed algorithm, it was shown how the algorithm was effective on finding the plate location in a given image. Results show that the different parameters tested were successful and converges to a point where the plate locations can be located. The algorithms were tested on an image of a vehicle equipped with a license plate on its frontal view tested on a large number of trials. The genetic algorithm initialized 2000 chromosomes as its initial population and a fixed generation's count of 100. It was observed that the time it took for the program to locate the plate is about 3 seconds. Another finding observed is that by varying the initial chromosome count and generation count will lead to longer computation time with increased accuracy. On the contrary, if the initial values were lessened, computation time will be less but the accuracy lessen. Results show that this plate localization technique successfully locates the plate and may be calibrated depending on the time of analysis. © 2016 IEEE. 2017-02-08T08:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/3356 info:doi/10.1109/TENCON.2016.7848557 https://animorepository.dlsu.edu.ph/context/faculty_research/article/4358/type/native/viewcontent/TENCON.2016.7848557 Faculty Research Work Animo Repository Vehicle detectors Genetic algorithms Image processing Manufacturing |
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Vehicle detectors Genetic algorithms Image processing Manufacturing Bedruz, Rhen Anjerome Sybingco, Edwin Bandala, Argel A. Quiros, Ana Riza Uy, Aaron Christian P. Dadios, Elmer P. Philippine vehicle plate localization using image thresholding and genetic algorithm |
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This paper proposes a vehicle plate localization method using genetic algorithm integrated with image thresholding. Image thresholding outputs a value which varies on the time the image is captured. Genetic algorithm on the other hand, executed the license plate region detection of the digital image which depends on the set-level of the image threshold values obtained. Using the proposed algorithm, it was shown how the algorithm was effective on finding the plate location in a given image. Results show that the different parameters tested were successful and converges to a point where the plate locations can be located. The algorithms were tested on an image of a vehicle equipped with a license plate on its frontal view tested on a large number of trials. The genetic algorithm initialized 2000 chromosomes as its initial population and a fixed generation's count of 100. It was observed that the time it took for the program to locate the plate is about 3 seconds. Another finding observed is that by varying the initial chromosome count and generation count will lead to longer computation time with increased accuracy. On the contrary, if the initial values were lessened, computation time will be less but the accuracy lessen. Results show that this plate localization technique successfully locates the plate and may be calibrated depending on the time of analysis. © 2016 IEEE. |
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Bedruz, Rhen Anjerome Sybingco, Edwin Bandala, Argel A. Quiros, Ana Riza Uy, Aaron Christian P. Dadios, Elmer P. |
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Bedruz, Rhen Anjerome Sybingco, Edwin Bandala, Argel A. Quiros, Ana Riza Uy, Aaron Christian P. Dadios, Elmer P. |
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Bedruz, Rhen Anjerome |
title |
Philippine vehicle plate localization using image thresholding and genetic algorithm |
title_short |
Philippine vehicle plate localization using image thresholding and genetic algorithm |
title_full |
Philippine vehicle plate localization using image thresholding and genetic algorithm |
title_fullStr |
Philippine vehicle plate localization using image thresholding and genetic algorithm |
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Philippine vehicle plate localization using image thresholding and genetic algorithm |
title_sort |
philippine vehicle plate localization using image thresholding and genetic algorithm |
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Animo Repository |
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2017 |
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https://animorepository.dlsu.edu.ph/faculty_research/3356 https://animorepository.dlsu.edu.ph/context/faculty_research/article/4358/type/native/viewcontent/TENCON.2016.7848557 |
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